Original Research Article

Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

Received:
Accepted:
Published online:

The members of the ClinGen Sequence Variant Interpretation Working Group (ClinGen SVI) are listed above the references.

Abstract

Purpose

We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning.

Methods

The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.

Results

We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).

Conclusion

By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

  • Subscribe to Genetics in Medicine for full access:

    $1066

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    , , et al. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat 2008;29:1282–1291.

  2. 2.

    , , et al. Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2. Am J Hum Genet 2004;75:535–544.

  3. 3.

    , , et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405–424.

  4. 4.

    , , et al. Analysis of missense variation in human BRCA1 in the context of interspecific sequence variation. J Med Genet 2004;41:492–507.

  5. 5.

    , , et al. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am J Hum Genet 2007;81:873–883.

  6. 6.

    , , . A full-likelihood method for the evaluation of causality of sequence variants from family data. Am J Hum Genet 2003;73:652–655.

  7. 7.

    , , et al. Classification of rare missense substitutions, using risk surfaces, with genetic- and molecular-epidemiology applications. Hum Mutat 2008;29:1342–1354.

  8. 8.

    , , et al. A multifactorial likelihood model for MMR gene variant classification incorporating probabilities based on sequence bioinformatics and tumor characteristics: a report from the Colon Cancer Family Registry. Hum Mutat 2013;34:200–209.

  9. 9.

    , , et al. Calibration of multiple in silico tools for predicting pathogenicity of mismatch repair gene missense substitutions. Hum Mutat 2013;34:255–265.

  10. 10.

    , , et al. Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database. Nat Genet 2014;46:107–115.

Download references

Acknowledgements

S.V.T. and M.S.G. are supported in part by R01 CA164944 (mismatch repair variants). S.V.T. and K.M.B. are supported in part by P30 CA042014 (Cancer Center Support grant). S.V.T. is supported in part by R01 CA121245 (BRCA gene variants). M.S.G. is supported in part by U01 HG007437 (Clinical Genome Resource). S.M.H. is supported in part by U41 HG006834 (Clinical Genome Resource). S.A.P. is supported in part by U01 HG007436 (Clinical Genome Resource). L.G.B. is supported in part by ZIA HG200387 03 and ZIA HG200388 03 (Intramural Research Program of the National Human Genome Research Institute).

Author information

Affiliations

  1. Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA

    • Sean V Tavtigian
  2. Department of Medicine and University of Vermont Cancer Center, University of Vermont Robert Larner, MD, College of Medicine, Burlington, Vermont, USA

    • Marc S Greenblatt
  3. Partners HealthCare Laboratory for Molecular Medicine and Harvard Medical School, Boston, Massachusetts, USA

    • Steven M Harrison
  4. Invitae, San Francisco, California, USA

    • Robert L Nussbaum
  5. Department of Genetics and Department of Biomedical Data Science, Stanford University, Palo Alto, California, USA

    • Snehit A Prabhu
  6. Division of Epidemiology and Huntsman Cancer Institute, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City Utah, USA

    • Kenneth M Boucher
  7. Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA

    • Leslie G Biesecker

Authors

  1. Search for Sean V Tavtigian in:

  2. Search for Marc S Greenblatt in:

  3. Search for Steven M Harrison in:

  4. Search for Robert L Nussbaum in:

  5. Search for Snehit A Prabhu in:

  6. Search for Kenneth M Boucher in:

  7. Search for Leslie G Biesecker in:

Competing interests

R.L.N. receives salary and equity from Invitae, serves as chair of the Rare Disease Therapeutic Area Scientific Review Panel for Pfizer, and is on the Advisory Board of Genome Medical. L.G.B. is an uncompensated adviser for Illumina. The other authors declare no conflict of interest.

Corresponding author

Correspondence to Sean V Tavtigian.

Supplementary information

The members of the Clinical Genome Resource, Sequence Variant Interpretation Working Group

Antonis Antoniou, Cambridge University, Cambridge, UK; Jonathan S. Berg, University of North Carolina, Chapel Hill, NC; Leslie G. Biesecker, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD; co-chair; Steven E. Brenner, University of California, Berkeley, Berkeley, CA; Fergus Couch, Mayo Clinic, Rochester, MN; Garry Cutting, Department of Human Genetics, Johns Hopkins University School of Medicine, Baltimore, MD; Marc S. Greenblatt, University of Vermont; Robert Larner, College of Medicine, Burlington, VT; Steven M. Harrison, Partners HealthCare Laboratory for Molecular Medicine and Harvard Medical School, Boston, MA; co-chair; Christopher D. Heinen, University of Connecticut Health, Farmington, CT; Matthew E. Hurles, Wellcome Trust Sanger Institute, Hinxton, UK; H. Peter Kang, Counsyl, San Francisco, CA; Rachel Karchin, Johns Hopkins University School of Medicine, Baltimore, MD; Robert L. Nussbaum, Invitae, San Francisco, CA; Sharon E. Plon, Baylor College of Medicine, Houston, TX; Heidi L. Rehm, Partners HealthCare Laboratory for Molecular Medicine and Harvard Medical School, Boston, MA; Sean V. Tavtigian, Department of Oncological Science and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT.

Supplementary material is linked to the online version of the paper at http://www.nature.com/gim